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Cabrera Alvargonzalez JJ, Larrañaga A, Martinez J, Pérez Castro S, Rey Cao S, Daviña Nuñez C, Del Campo Pérez V, Duran Parrondo C, Suarez Luque S, González Alonso E, Silva Tojo AJ, Porteiro J, Regueiro B. Assessment of the Effective Sensitivity of SARS-CoV-2 Sample Pooling Based on a Large-Scale Screening Experience: Retrospective Analysis. JMIR Public Health Surveill 2024; 10:e54503. [PMID: 39316785 PMCID: PMC11462102 DOI: 10.2196/54503] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/19/2023] [Revised: 05/18/2024] [Accepted: 07/18/2024] [Indexed: 09/26/2024] Open
Abstract
BACKGROUND The development of new large-scale saliva pooling detection strategies can significantly enhance testing capacity and frequency for asymptomatic individuals, which is crucial for containing SARS-CoV-2. OBJECTIVE This study aims to implement and scale-up a SARS-CoV-2 screening method using pooled saliva samples to control the virus in critical areas and assess its effectiveness in detecting asymptomatic infections. METHODS Between August 2020 and February 2022, our laboratory received a total of 928,357 samples. Participants collected at least 1 mL of saliva using a self-sampling kit and registered their samples via a smartphone app. All samples were directly processed using AutoMate 2550 for preanalytical steps and then transferred to Microlab STAR, managed with the HAMILTON Pooling software for pooling. The standard pool preset size was 20 samples but was adjusted to 5 when the prevalence exceeded 2% in any group. Real-time polymerase chain reaction (RT-PCR) was conducted using the Allplex SARS-CoV-2 Assay until July 2021, followed by the Allplex SARS-CoV-2 FluA/FluB/RSV assay for the remainder of the study period. RESULTS Of the 928,357 samples received, 887,926 (95.64%) were fully processed into 56,126 pools. Of these pools, 4863 tested positive, detecting 5720 asymptomatic infections. This allowed for a comprehensive analysis of pooling's impact on RT-PCR sensitivity and false-negative rate (FNR), including data on positive samples per pool (PPP). We defined Ctref as the minimum cycle threshold (Ct) of each data set from a sample or pool and compared these Ctref results from pooled samples with those of the individual tests (ΔCtP). We then examined their deviation from the expected offset due to dilution [ΔΔCtP = ΔCtP - log2]. In this work, the ΔCtP and ΔΔCtP were 2.23 versus 3.33 and -0.89 versus 0.23, respectively, comparing global results with results for pools with 1 positive sample per pool. Therefore, depending on the number of genes used in the test and the size of the pool, we can evaluate the FNR and effective sensitivity (1 - FNR) of the test configuration. In our scenario, with a maximum of 20 samples per pool and 3 target genes, statistical observations indicated an effective sensitivity exceeding 99%. From an economic perspective, the focus is on pooling efficiency, measured by the effective number of persons that can be tested with 1 test, referred to as persons per test (PPT). In this study, the global PPT was 8.66, reflecting savings of over 20 million euros (US $22 million) based on our reagent prices. CONCLUSIONS Our results demonstrate that, as expected, pooling reduces the sensitivity of RT-PCR. However, with the appropriate pool size and the use of multiple target genes, effective sensitivity can remain above 99%. Saliva pooling may be a valuable tool for screening and surveillance in asymptomatic individuals and can aid in controlling SARS-CoV-2 transmission. Further studies are needed to assess the effectiveness of these strategies for SARS-CoV-2 and their application to other microorganisms or biomarkers detected by PCR.
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Affiliation(s)
- Jorge J Cabrera Alvargonzalez
- Microbiology Department, Complexo Hospitalario Universitario de Vigo, Servicio Galego de Saude, Vigo, Spain
- Galicia Sur Health Research Institute (IIS Galicia Sur), Microbiology and Infectology Research Group, Vigo, Spain
| | - Ana Larrañaga
- Centro de Investigación en Tecnologías, Energía y Procesos Industriales, University of Vigo, Lagoas-Marcosende, Vigo, Spain
| | - Javier Martinez
- Applied Mathematics I, Telecommunications Engineering School, University of Vigo, Vigo, Spain
| | - Sonia Pérez Castro
- Microbiology Department, Complexo Hospitalario Universitario de Vigo, Servicio Galego de Saude, Vigo, Spain
- Galicia Sur Health Research Institute (IIS Galicia Sur), Microbiology and Infectology Research Group, Vigo, Spain
| | - Sonia Rey Cao
- Microbiology Department, Complexo Hospitalario Universitario de Vigo, Servicio Galego de Saude, Vigo, Spain
- Galicia Sur Health Research Institute (IIS Galicia Sur), Microbiology and Infectology Research Group, Vigo, Spain
| | - Carlos Daviña Nuñez
- Galicia Sur Health Research Institute (IIS Galicia Sur), Microbiology and Infectology Research Group, Vigo, Spain
| | - Víctor Del Campo Pérez
- Department of Preventive Medicine and Public Health, Complexo Hospitalario, Universitario de Vigo, Vigo, Spain
| | - Carmen Duran Parrondo
- Dirección Xeral de Saúde Pública, Consellería de Sanidade, Xunta de Galicia, Santiago de Compostela, Spain
| | - Silvia Suarez Luque
- Dirección Xeral de Saúde Pública, Consellería de Sanidade, Xunta de Galicia, Santiago de Compostela, Spain
| | - Elena González Alonso
- Galicia Sur Health Research Institute (IIS Galicia Sur), Microbiology and Infectology Research Group, Vigo, Spain
| | - Alfredo José Silva Tojo
- Dirección Xeral de Maiores y atención Sociosanitaria, Conselleria de Politica Social e Xuventude, Xunta de Galicia, Santiago de Compostela, Spain
| | - Jacobo Porteiro
- Centro de Investigación en Tecnologías, Energía y Procesos Industriales, University of Vigo, Lagoas-Marcosende, Vigo, Spain
| | - Benito Regueiro
- Microbiology Department, Complexo Hospitalario Universitario de Vigo, Servicio Galego de Saude, Vigo, Spain
- Galicia Sur Health Research Institute (IIS Galicia Sur), Microbiology and Infectology Research Group, Vigo, Spain
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Riou J, Studer E, Fesser A, Schuster TM, Low N, Egger M, Hauser A. Surveillance of SARS-CoV-2 prevalence from repeated pooled testing: application to Swiss routine data. Epidemiol Infect 2024; 152:e100. [PMID: 39168632 DOI: 10.1017/s0950268824000876] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 08/23/2024] Open
Abstract
Surveillance of SARS-CoV-2 through reported positive RT-PCR tests is biased due to non-random testing. Prevalence estimation in population-based samples corrects for this bias. Within this context, the pooled testing design offers many advantages, but several challenges remain with regards to the analysis of such data. We developed a Bayesian model aimed at estimating the prevalence of infection from repeated pooled testing data while (i) correcting for test sensitivity; (ii) propagating the uncertainty in test sensitivity; and (iii) including correlation over time and space. We validated the model in simulated scenarios, showing that the model is reliable when the sample size is at least 500, the pool size below 20, and the true prevalence below 5%. We applied the model to 1.49 million pooled tests collected in Switzerland in 2021-2022 in schools, care centres, and workplaces. We identified similar dynamics in all three settings, with prevalence peaking at 4-5% during winter 2022. We also identified differences across regions. Prevalence estimates in schools were correlated with reported cases, hospitalizations, and deaths (coefficient 0.84 to 0.90). We conclude that in many practical situations, the pooled test design is a reliable and affordable alternative for the surveillance of SARS-CoV-2 and other viruses.
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Affiliation(s)
- Julien Riou
- Institute of Social and Preventive Medicine, University of Bern, Bern, Switzerland
- Department of Epidemiology and Health Systems, Unisanté, Center for Primary Care and Public Health & University of Lausanne, Lausanne, Switzerland
| | - Erik Studer
- Federal Office of Public Health, Liebefeld, Switzerland
| | - Anna Fesser
- Federal Office of Public Health, Liebefeld, Switzerland
| | | | - Nicola Low
- Institute of Social and Preventive Medicine, University of Bern, Bern, Switzerland
| | - Matthias Egger
- Institute of Social and Preventive Medicine, University of Bern, Bern, Switzerland
- Centre for Infectious Disease Epidemiology and Research, Faculty of Health Sciences, University of Cape Town, Cape Town, South Africa
- Population Health Sciences, Bristol Medical School, University of Bristol, Bristol, UK
| | - Anthony Hauser
- Institute of Social and Preventive Medicine, University of Bern, Bern, Switzerland
- INSERM, Sorbonne Université, Pierre Louis Institute of Epidemiology and Public Health, Paris, France
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Wu TY, Liao YC, Fuh CS, Weng PW, Wang JY, Chen CY, Huang YM, Chen CP, Chu YL, Chen CK, Yeh KL, Yu CH, Wu HK, Lin WP, Liou TH, Wu MS, Liaw CK. An improvement of current hypercube pooling PCR tests for SARS-CoV-2 detection. Front Public Health 2022; 10:994712. [PMID: 36339215 PMCID: PMC9627488 DOI: 10.3389/fpubh.2022.994712] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/15/2022] [Accepted: 09/20/2022] [Indexed: 01/26/2023] Open
Abstract
The severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) pandemic can be effectively controlled by rapid and accurate identification of SARS-CoV-2-infected cases through large-scale screening. Hypercube pooling polymerase chain reaction (PCR) is frequently used as a pooling technique because of its high speed and efficiency. We attempted to implement the hypercube pooling strategy and found it had a large quantization effect. This raised two questions: is hypercube pooling with edge = 3 actually the optimal strategy? If not, what is the best edge and dimension? We used a C++ program to calculate the expected number of PCR tests per patient for different values of prevalence, edge, and dimension. The results showed that every edge had a best performance range. Then, using C++ again, we created a program to calculate the optimal edge and dimension required for pooling samples when entering prevalence into our program. Our program will be provided as freeware in the hope that it can help governments fight the SARS-CoV-2 pandemic.
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Affiliation(s)
- Tai-Yin Wu
- Department of Family Medicine, Zhongxing Branch, Taipei City Hospital, Taipei, Taiwan
- Institute of Epidemiology and Preventive Medicine, National Taiwan University, Taipei, Taiwan
- General Education Center, University of Taipei, Taipei, Taiwan
| | - Yu-Ciao Liao
- Institute of Computer Science and Information Engineering, National Taiwan University, Taipei, Taiwan
| | - Chiou-Shann Fuh
- Institute of Computer Science and Information Engineering, National Taiwan University, Taipei, Taiwan
| | - Pei-Wei Weng
- Department of Orthopedics, School of Medicine, College of Medicine, Taipei Medical University, Taipei, Taiwan
- Department of Orthopedics, Shuang Ho Hospital, Taipei Medical University, New Taipei, Taiwan
- Graduate Institute of Biomedical Optomechatronics, College of Biomedical Engineering, Research Center of Biomedical Device, Taipei Medical University, Taipei, Taiwan
| | - Jr-Yi Wang
- Department of Orthopedics, School of Medicine, College of Medicine, Taipei Medical University, Taipei, Taiwan
- Department of Orthopedics, Shuang Ho Hospital, Taipei Medical University, New Taipei, Taiwan
| | - Chih-Yu Chen
- Department of Orthopedics, School of Medicine, College of Medicine, Taipei Medical University, Taipei, Taiwan
- Department of Orthopedics, Shuang Ho Hospital, Taipei Medical University, New Taipei, Taiwan
- Graduate Institute of Biomedical Optomechatronics, College of Biomedical Engineering, Research Center of Biomedical Device, Taipei Medical University, Taipei, Taiwan
- International Ph.D. Program in Biomedical Engineering, College of Biomedical Engineering, Taipei Medical University, Taipei, Taiwan
| | - Yu-Min Huang
- Department of Orthopedics, School of Medicine, College of Medicine, Taipei Medical University, Taipei, Taiwan
- Department of Orthopedics, Shuang Ho Hospital, Taipei Medical University, New Taipei, Taiwan
| | - Chung-Pei Chen
- Department of Orthopedics, Cathay General Hospital, Taipei, Taiwan
| | - Yo-Lun Chu
- Department of Orthopedics, Shin Kong Wu Ho-Su Memorial Hospital, Taipei, Taiwan
- School of Medicine, College of Medicine, Fu Jen Catholic University, Taipei, Taiwan
- Department of Biomedical Engineering, National Taiwan University, Taipei, Taiwan
| | - Cheng-Kuang Chen
- Department of Orthopedics, Shin Kong Wu Ho-Su Memorial Hospital, Taipei, Taiwan
- Department of Biomedical Engineering, National Taiwan University, Taipei, Taiwan
| | - Kuei-Lin Yeh
- Institute of Computer Science and Information Engineering, National Taiwan University, Taipei, Taiwan
- Department of Orthopaedics, Ditmanson Medical Foundation Chia-Yi Christian Hospital, Chiayi, Taiwan
- Department of Long-Term Care and Management, WuFeng University, Chiayi, Taiwan
| | - Ching-Hsiao Yu
- Department of Orthopaedic Surgery, Taoyuan General Hospital, Ministry of Health and Welfare, Taoyuan, Taiwan
- Department of Orthopaedic Surgery, National Taiwan University Hospital, Taipei, Taiwan
| | - Hung-Kang Wu
- Department of Orthopaedic Surgery, Taoyuan General Hospital, Ministry of Health and Welfare, Taoyuan, Taiwan
- Department of Orthopaedic Surgery, National Taiwan University Hospital, Taipei, Taiwan
- Department of Nursing, Yuanpei University of Medical Technology, Hsinchu, Taiwan
| | - Wei-Peng Lin
- Department of Orthopaedic Surgery, National Taiwan University Hospital, Taipei, Taiwan
- Department of Orthopedics, Postal Hospital, Taipei, Taiwan
| | - Tsan-Hon Liou
- Department of Physical Medicine and Rehabilitation, School of Medicine, College of Medicine, Taipei Medical University, Taipei, Taiwan
| | - Mai-Szu Wu
- Division of Nephrology, School of Medicine, College of Medicine, Taipei Medical University, Taipei, Taiwan
| | - Chen-Kun Liaw
- Department of Orthopedics, School of Medicine, College of Medicine, Taipei Medical University, Taipei, Taiwan
- Department of Orthopedics, Shuang Ho Hospital, Taipei Medical University, New Taipei, Taiwan
- Graduate Institute of Biomedical Optomechatronics, College of Biomedical Engineering, Research Center of Biomedical Device, Taipei Medical University, Taipei, Taiwan
- TMU Biodesign Center, Taipei Medical University, Taipei, Taiwan
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